1 Exploring register variation in learner lexis The high-frequency verb make in native and learner speech and writing Claire Hugon CECL Louvain-la-Neuve 24. January 2008

2 Outline of the presentation  Background and aims of the study  Methodology  Setting the scene: make in the BNC  Make in native and French-speaking learner speech and writing  Methodological implications and avenues for future research

3 Background and aims of the study  Broader context: PhD research on the acquisition of high-frequency verbs  3 preliminary remarks: The influence of L1 as the « darling variable » of learner corpus linguists Learner writing is frequently said to be speech- like SLA variables are often studied in isolation

4 Background and aims of the study  Research questions: Does register have an influence on the use of high-frequency verbs (HFVs) such as make in learner English? Is the use of make in learner writing similar to native speech? Can register differences be an alternative/ complementary explanation to features of non- nativeness attributed to L1?

5 Methodology

6 Confrontation of native and learner data to detect similarities and differences and try to explain them (to-ing and fro-ing between the two components) NS (writing) LOCNESS NS (speech) LOCNEC NNS (writing) ICLE-FR NNS (speech) LINDSEI-FR

7 Implementing the methodology: the example of make 1. native language: make (and other HFVs) in the BNC  see how HFVs behave in native language before looking for differences in learner language  BNC: wide-coverage corpus, much larger than LOCNESS  better suited for broad, quantitative analysis 2. quantitative and qualitative analysis: make in native and learner speech and writing  native: LOCNESS and LOCNEC  learner: ICLE-FR and LINDSEI-FR  Comparison of the results

8 Top HFVs in the BNC Whole BNCSpoken BNCWritten BNC 1. say1. get1. say 2. go 2. make (rel.freq. 2,190) 3. get3. say3. go 4. make (rel.freq.2,160)4. know4. take 5. seem5. think5. see 6. know6. see6. get 7. take7. come7. know 8. think8. mean8. come 9. come9. want9. give 10. give10. take10. use 11. look 11. think 12. use12. make (rel.freq. 1,905)12. look

9 Make in the BNC spoken BNC (/million words) written BNC (/million words) chi-square value 1,9052,190*** Make is less frequent in speech than in writing the difference is highly significant according to the chi-square test atypical (most HFVs are more typical of speech)

10 Implementing the methodology: the exampe of make 1. native language: make (and other HFVs) in the BNC  see how HFVs behave in native language before looking for differences in learner language  BNC: wide-coverage corpus, much larger than LOCNESS  better suited for broad, quantitative analysis 2. quantitative and qualitative analysis: make in native and learner speech and writing  native: LOCNESS and LOCNEC  learner: ICLE-FR and LINDSEI-FR  Comparison of the results

11 Make in native and learner speech and writing: some findings > ≥ 126.6NS vs NNS < < Speech vs writing NNS NS WritingSpeech Overall frequency (/100,000 words): highly significant (***) underuse of make in NNS writing  brings frequency in NNS writing closer to NS speech slight underuse of make in NNS speech, but not significant make is significantly(***) less frequent in NNS speech than in NNS writing make is significantly (***) less frequent in NS speech than in NS writing Make is a polysemous verb  qualitative analysis to explain the results

12 7 main semantic subdivisions  core meaning (produce, create)  delexical uses ‘speech’ collocates other collocates  causative uses causative uses  make + adj  make + verb  make + noun ‘money’ make phrasal verbs other uses link verbs

13 Distribution of the occurrences of make in the four corpora, by semantic category

14 Delexical uses of make > < 42.9NS vs NNS 42.9 < < Speech vs writing NNS NS WritingSpeech Overall frequency (/100,000 words): highly significant (***) underuse of make in NNS writing significant (*) overuse in NNS speech significantly(***) less frequent in NNS speech than in NNS writing significantly (***) less frequent in NS speech than in NS writing

15 Delexical uses of make è NNS writing: underuse of EAP delexical structures (make a case, make a statement) è maybe register-related è NNS speech: overuse of delexical uses è probably communication strategy (pressure, online processing, make as default verb): è especially one course we have to make erm. a kind of work è when I go. eat em. with my master the: the cooking he made for us is just er. è about er.. an.. experience which I.. made when I was in first candi

16 Causative uses of make > > 24.2NS vs NNS 24.2 < < Speech vs writing NNS NS WritingSpeech Overall frequency (/100,000 words): significant (**) underuse in NNS writing significant (***) underuse in NNS speech significantly(***) less frequent in NNS speech than in NNS writing significantly (***) less frequent in NS speech than in NS writing

17 Causative uses of make è underuse of causative structures as a whole in learner language (both in speech and in writing) è 3 causative structures: make + adjective (make sth easier) make + verb (make someone feel bad) make + noun (make someone an outcast)

18 The proportion of each category is remarkably similar for NS and NNS registers NS writingNNS writing NS speechNNS speech Adjective57%57.6%39%40.9% Verb30%32.7%54.5%50% Noun13%9.7%6.5%9.1% Total100%

19 Some previous findings about make: 1. French-speaking and Swedish-speaking learners underuse make in delexical structures (Altenberg & Granger 2001, Altenberg 2001) 2. Swedish-speaking learners overuse causative make + adj and make + verb (Altenberg 2002a, 2002b) (Partially) L1-related explanations: 1. delexical structures: avoidance strategy due to arbitrary and L1-specific choice of the verb 2. causative structures: transfer of frequency from L1 + overgeneralisation

20 Plausible register-related explanation? 1. delexical combinations:yes. Transfer and register have a similar impact. Underuse of delexical structures in NNS writing: much less frequent in NS speech than in NS writing: possible transfer of frequency from target language speech 2. causative structures: no (at least not for Swedish-speaking learners). Transfer and register seem to pull in opposite directions: L1 Swedish causes overuse of L2 English ADJ and VERB causative structures English speech uses fewer causatives structures, so poor register awareness is not a valid explanation for the Swedish-speaking NNS’observed overuse of causative structures.

21 To sum up: Make is a multi-faceted verb with many meanings, functions, and patterns: a very interesting picture of scale of proficiency of advanced interlanguage emerges  from no knowledge at all (e.g. some phrasal verbs, link verb uses, ‘money’ make are nearly absent)  to near-perfect knowledge (e.g. proportions of 3 causative syntactic structures)  including various levels of partial knowledge (e.g. core uses, delexical uses, overall frequency of causative uses, etc.)  knowing a word is not an all-or-nothing matter

22 Methodological implications  The results can be partially skewed by one part of the interview:  e.g. for the core meaning of make (= produce, create), overuse in LINDSEI-FR due to picture description task NS: do/draw a portrait, do/paint a picture  he paints the picture of a beautiful woman NNS: make a portrait/a drawing/ a picture  there is a painter he’s making a portrait the portrait of a of a girl

23 Methodological implications  e.g. for the causative make + V structure, in LOCNEC 16 instances/42 involve look: he’s now repainting it making her look. much more attractive he makes her look. totally different makes her look very glamorous  clearly topic-induced by picture description which elicits predictable patterns  bears unduly on the overall results for that category  not mirrored in LINDSEI-FR (1/11)  probably more appropriate to study the picture description (elicited) separately from the more spontaneous tasks

24 Where to from here? Possible avenues for further research  Complement quantitative analysis of native English HFVs by carrying out a similar analysis on learner data (requires preparation of the data, e.g. tagging of LINDSEI)  Combine corpus data with other types of data (e.g. elicitation)  Complement qualitative analysis of make by carrying out similar analyses of other HFVs reach better understanding of how these complex verbs are gradually acquired in the interlanguage system  Study other variables: L1: Carry out transfer analysis on the same data + other learner populations Proficiency: longitudinal approach (data from other proficiency levels)  also help to understand the gradual evolution of the interlanguage system in time

25 Thank you!